Lockman SWIRE master catalogue

Checks and diagnostics

from herschelhelp_internal import git_version
print("This notebook was run with herschelhelp_internal version: \n{}".format(git_version()))

This notebook was run with herschelhelp_internal version: 
04829ed (Thu Nov 2 16:57:19 2017 +0000)

%matplotlib inline
#%config InlineBackend.figure_format = 'svg'

import matplotlib.pyplot as plt
plt.rc('figure', figsize=(10, 6))
plt.style.use('ggplot')

import locale
locale.setlocale(locale.LC_ALL, 'en_GB')

import os
import time
import itertools

from astropy.coordinates import SkyCoord
from astropy.table import Table
from astropy import units as u
from astropy import visualization as vis
import numpy as np
from matplotlib_venn import venn3

from herschelhelp_internal.masterlist import nb_compare_mags, nb_ccplots, nb_histograms, find_last_ml_suffix

OUT_DIR = os.environ.get('OUT_DIR', "./data")
SUFFIX = find_last_ml_suffix()
#SUFFIX = "20170710"

master_catalogue_filename = "master_catalogue_lockman-swire_{}.fits".format(SUFFIX)
master_catalogue = Table.read("{}/{}".format(OUT_DIR, master_catalogue_filename))

print("Diagnostics done using: {}".format(master_catalogue_filename))

Diagnostics done using: master_catalogue_lockman-swire_20170817.fits

I - Summary of wavelength domains

flag_obs = master_catalogue['flag_optnir_obs']
flag_det = master_catalogue['flag_optnir_det']

venn3(
    [
        np.sum(flag_obs == 4),
        np.sum(flag_obs == 2),
        np.sum(flag_obs == 6),
        np.sum(flag_obs == 1),
        np.sum(flag_obs == 5),
        np.sum(flag_obs == 3),
        np.sum(flag_obs == 7)
    ],
    set_labels=('Optical', 'near-IR', 'mid-IR'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/len(flag_obs)))
)
plt.title("Wavelength domain observations");

venn3(
    [
        np.sum(flag_det[flag_obs == 7] == 4),
        np.sum(flag_det[flag_obs == 7] == 2),
        np.sum(flag_det[flag_obs == 7] == 6),
        np.sum(flag_det[flag_obs == 7] == 1),
        np.sum(flag_det[flag_obs == 7] == 5),
        np.sum(flag_det[flag_obs == 7] == 3),
        np.sum(flag_det[flag_obs == 7] == 7)
    ],
    set_labels=('mid-IR', 'near-IR', 'Optical'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/np.sum(flag_det != 0)))
)
plt.title("Detection of the {} sources detected\n in any wavelength domains "
          "(among {} sources)".format(
              locale.format('%d', np.sum(flag_det != 0), grouping=True),
              locale.format('%d', len(flag_det), grouping=True)));

II - Comparing magnitudes in similar filters

The master list if composed of several catalogues containing magnitudes in similar filters on different instruments. We are comparing the magnitudes in these corresponding filters.

u_bands = ["WFC u",          "CFHT Megacam u"]
g_bands = ["WFC g", "RCS g", "CFHT Megacam g",  "GPC1 g"]
r_bands = ["WFC r", "RCS r", "CFHT Megacam r",  "GPC1 r"]
i_bands = ["WFC i", "RCS i",                    "GPC1 i"]
z_bands = ["WFC z", "RCS z", "CFHT Megacam z",  "GPC1 z"]
y_bands = [         "RCS y",                    "GPC1 y"]

II.a - Comparing depths

We compare the histograms of the total aperture magnitudes of similar bands.

for bands in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    colnames = ["m_{}".format(band.replace(" ", "_").lower()) for band in bands]
    nb_histograms(master_catalogue, colnames, bands)

II.b - Comparing magnitudes

We compare one to one each magnitude in similar bands.

for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    for band1, band2 in itertools.combinations(band_of_a_kind, 2):
        
        basecol1, basecol2 = band1.replace(" ", "_").lower(), band2.replace(" ", "_").lower()
        
        col1, col2 = "m_ap_{}".format(basecol1), "m_ap_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (aperture)".format(band1), "{} (aperture)".format(band2)))
        
        col1, col2 = "m_{}".format(basecol1), "m_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (total)".format(band1), "{} (total)".format(band2)))

CFHT Megacam u (aperture) - WFC u (aperture):
- Median: -0.06
- Median Absolute Deviation: 0.25
- 1% percentile: -0.94939359664917
- 99% percentile: 3.2226499748230046

CFHT Megacam u (total) - WFC u (total):
- Median: -0.27
- Median Absolute Deviation: 0.21
- 1% percentile: -1.6238471603393554
- 99% percentile: 1.9827452659606981

No sources have both WFC g (aperture) and RCS g (aperture) values.
RCS g (total) - WFC g (total):
- Median: -0.08
- Median Absolute Deviation: 0.18
- 1% percentile: -0.951687240600586
- 99% percentile: 1.1384436035156251

CFHT Megacam g (aperture) - WFC g (aperture):
- Median: 0.11
- Median Absolute Deviation: 0.14
- 1% percentile: -0.5077003479003905
- 99% percentile: 0.8677662658691405

CFHT Megacam g (total) - WFC g (total):
- Median: -0.04
- Median Absolute Deviation: 0.15
- 1% percentile: -0.8756649780273438
- 99% percentile: 1.4227341461181653

GPC1 g (aperture) - WFC g (aperture):
- Median: -0.12
- Median Absolute Deviation: 0.25
- 1% percentile: -1.6477296829223633
- 99% percentile: 2.3155704498290794

GPC1 g (total) - WFC g (total):
- Median: -0.02
- Median Absolute Deviation: 0.18
- 1% percentile: -2.03228515625
- 99% percentile: 0.8685866928100586

No sources have both RCS g (aperture) and CFHT Megacam g (aperture) values.
CFHT Megacam g (total) - RCS g (total):
- Median: 0.05
- Median Absolute Deviation: 0.18
- 1% percentile: -1.0380179977416992
- 99% percentile: 1.2724957084655744

No sources have both RCS g (aperture) and GPC1 g (aperture) values.
GPC1 g (total) - RCS g (total):
- Median: 0.09
- Median Absolute Deviation: 0.20
- 1% percentile: -2.1646539306640626
- 99% percentile: 0.9680083465576188

GPC1 g (aperture) - CFHT Megacam g (aperture):
- Median: -0.27
- Median Absolute Deviation: 0.35
- 1% percentile: -2.4312880706787108
- 99% percentile: 2.318428840637206

GPC1 g (total) - CFHT Megacam g (total):
- Median: 0.05
- Median Absolute Deviation: 0.18
- 1% percentile: -2.6805446624755858
- 99% percentile: 0.911133003234862

No sources have both WFC r (aperture) and RCS r (aperture) values.
RCS r (total) - WFC r (total):
- Median: -0.13
- Median Absolute Deviation: 0.15
- 1% percentile: -1.020458526611328
- 99% percentile: 1.0779574584960954

CFHT Megacam r (aperture) - WFC r (aperture):
- Median: 0.12
- Median Absolute Deviation: 0.15
- 1% percentile: -0.5452775573730468
- 99% percentile: 1.1373986816406252

CFHT Megacam r (total) - WFC r (total):
- Median: -0.08
- Median Absolute Deviation: 0.14
- 1% percentile: -0.9584978103637695
- 99% percentile: 1.5919403076171919

GPC1 r (aperture) - WFC r (aperture):
- Median: -0.08
- Median Absolute Deviation: 0.14
- 1% percentile: -1.083700180053711
- 99% percentile: 1.2235185241699347

GPC1 r (total) - WFC r (total):
- Median: 0.01
- Median Absolute Deviation: 0.13
- 1% percentile: -1.3796411895751954
- 99% percentile: 0.8283004760742188

No sources have both RCS r (aperture) and CFHT Megacam r (aperture) values.
CFHT Megacam r (total) - RCS r (total):
- Median: 0.07
- Median Absolute Deviation: 0.17
- 1% percentile: -1.0872340965270995
- 99% percentile: 1.341750965118408

No sources have both RCS r (aperture) and GPC1 r (aperture) values.
GPC1 r (total) - RCS r (total):
- Median: 0.15
- Median Absolute Deviation: 0.12
- 1% percentile: -1.4188003540039062
- 99% percentile: 0.8836927032470658

GPC1 r (aperture) - CFHT Megacam r (aperture):
- Median: -0.23
- Median Absolute Deviation: 0.24
- 1% percentile: -2.0773930168151855
- 99% percentile: 1.1670617103576837

GPC1 r (total) - CFHT Megacam r (total):
- Median: 0.09
- Median Absolute Deviation: 0.12
- 1% percentile: -2.179352283477783
- 99% percentile: 0.8217307281494138

No sources have both WFC i (aperture) and RCS i (aperture) values.
RCS i (total) - WFC i (total):
- Median: -0.08
- Median Absolute Deviation: 0.20
- 1% percentile: -0.9838282966613769
- 99% percentile: 1.3779298400878914

GPC1 i (aperture) - WFC i (aperture):
- Median: -0.01
- Median Absolute Deviation: 0.14
- 1% percentile: -0.8346004486083984
- 99% percentile: 1.213294830322265

GPC1 i (total) - WFC i (total):
- Median: 0.08
- Median Absolute Deviation: 0.14
- 1% percentile: -0.8180191040039062
- 99% percentile: 1.0501995086669922

No sources have both RCS i (aperture) and GPC1 i (aperture) values.
GPC1 i (total) - RCS i (total):
- Median: 0.20
- Median Absolute Deviation: 0.11
- 1% percentile: -0.6361136627197265
- 99% percentile: 0.8754997253417969

No sources have both WFC z (aperture) and RCS z (aperture) values.
RCS z (total) - WFC z (total):
- Median: -0.05
- Median Absolute Deviation: 0.24
- 1% percentile: -1.0027453994750977
- 99% percentile: 1.752888126373291

CFHT Megacam z (aperture) - WFC z (aperture):
- Median: 0.14
- Median Absolute Deviation: 0.17
- 1% percentile: -0.6399097442626953
- 99% percentile: 3.041360149383542

CFHT Megacam z (total) - WFC z (total):
- Median: -0.04
- Median Absolute Deviation: 0.22
- 1% percentile: -0.9714330673217774
- 99% percentile: 2.1101235580444326

GPC1 z (aperture) - WFC z (aperture):
- Median: 0.02
- Median Absolute Deviation: 0.14
- 1% percentile: -0.8126788330078125
- 99% percentile: 1.2011602020263696

GPC1 z (total) - WFC z (total):
- Median: 0.10
- Median Absolute Deviation: 0.19
- 1% percentile: -0.8380362510681152
- 99% percentile: 1.3818613624572755

No sources have both RCS z (aperture) and CFHT Megacam z (aperture) values.
CFHT Megacam z (total) - RCS z (total):
- Median: 0.05
- Median Absolute Deviation: 0.24
- 1% percentile: -1.2459497833251953
- 99% percentile: 2.295762405395507

No sources have both RCS z (aperture) and GPC1 z (aperture) values.
GPC1 z (total) - RCS z (total):
- Median: 0.18
- Median Absolute Deviation: 0.13
- 1% percentile: -0.7215998649597168
- 99% percentile: 0.9408991813659668

GPC1 z (aperture) - CFHT Megacam z (aperture):
- Median: -0.17
- Median Absolute Deviation: 0.24
- 1% percentile: -2.4983787155151367
- 99% percentile: 1.8417782592773433

GPC1 z (total) - CFHT Megacam z (total):
- Median: 0.15
- Median Absolute Deviation: 0.13
- 1% percentile: -1.7105945587158204
- 99% percentile: 0.9008941650390576

No sources have both RCS y (aperture) and GPC1 y (aperture) values.
GPC1 y (total) - RCS y (total):
- Median: -0.35
- Median Absolute Deviation: 0.29
- 1% percentile: -1.709545249938965
- 99% percentile: 0.692159347534179

III - Comparing magnitudes to reference bands

Cross-match the master list to SDSS and 2MASS to compare its magnitudes to SDSS and 2MASS ones.

master_catalogue_coords = SkyCoord(master_catalogue['ra'], master_catalogue['dec'])

III.a - Comparing u, g, r, i, and z bands to SDSS

The catalogue is cross-matched to SDSS-DR13 withing 0.2 arcsecond.

We compare the u, g, r, i, and z magnitudes to those from SDSS using fiberMag for the aperture magnitude and petroMag for the total magnitude.

sdss = Table.read("../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_Lockman-SWIRE.fits")
sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)

idx, d2d, _ = sdss_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

sdss = sdss[mask]
ml_sdss_idx = idx[mask]

for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands]:
    for band in band_of_a_kind:
        
        sdss_mag_ap = sdss["fiberMag_{}".format(band[-1])]
        master_cat_mag_ap = master_catalogue["m_ap_{}".format(band.replace(" ", "_").lower())][ml_sdss_idx]
    
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (fiberMag)".format(band[-1]), "{} (aperture)".format(band)))
    
        sdss_mag_tot = sdss["petroMag_{}".format(band[-1])]
        master_cat_mag_tot = master_catalogue["m_ap_{}".format(band.replace(" ", "_").lower())][ml_sdss_idx]
        
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (petroMag)".format(band[-1]), "{} (total)".format(band)))

WFC u (aperture) - SDSS u (fiberMag):
- Median: -0.31
- Median Absolute Deviation: 0.18
- 1% percentile: -1.5710824012756348
- 99% percentile: 0.7945964813232422

WFC u (total) - SDSS u (petroMag):
- Median: -0.31
- Median Absolute Deviation: 0.18
- 1% percentile: -1.5710824012756348
- 99% percentile: 0.7945964813232422

CFHT Megacam u (aperture) - SDSS u (fiberMag):
- Median: 0.01
- Median Absolute Deviation: 0.52
- 1% percentile: -1.7244149017333983
- 99% percentile: 3.0332967376708986

CFHT Megacam u (total) - SDSS u (petroMag):
- Median: 0.01
- Median Absolute Deviation: 0.52
- 1% percentile: -1.7244149017333983
- 99% percentile: 3.0332967376708986

WFC g (aperture) - SDSS g (fiberMag):
- Median: -0.45
- Median Absolute Deviation: 0.12
- 1% percentile: -1.1531206893920898
- 99% percentile: 0.42618650436401384

WFC g (total) - SDSS g (petroMag):
- Median: -0.45
- Median Absolute Deviation: 0.12
- 1% percentile: -1.1531206893920898
- 99% percentile: 0.42618650436401384

No sources have both SDSS g (fiberMag) and RCS g (aperture) values.
No sources have both SDSS g (petroMag) and RCS g (total) values.
CFHT Megacam g (aperture) - SDSS g (fiberMag):
- Median: -0.29
- Median Absolute Deviation: 0.18
- 1% percentile: -1.1000246047973632
- 99% percentile: 1.2589447402954057

CFHT Megacam g (total) - SDSS g (petroMag):
- Median: -0.29
- Median Absolute Deviation: 0.18
- 1% percentile: -1.1000246047973632
- 99% percentile: 1.2589447402954057

GPC1 g (aperture) - SDSS g (fiberMag):
- Median: -0.54
- Median Absolute Deviation: 0.24
- 1% percentile: -1.8682982444763183
- 99% percentile: 1.794014415740968

GPC1 g (total) - SDSS g (petroMag):
- Median: -0.54
- Median Absolute Deviation: 0.24
- 1% percentile: -1.8682982444763183
- 99% percentile: 1.794014415740968

WFC r (aperture) - SDSS r (fiberMag):
- Median: -0.35
- Median Absolute Deviation: 0.07
- 1% percentile: -0.9245457077026367
- 99% percentile: 0.32145620346069204

WFC r (total) - SDSS r (petroMag):
- Median: -0.35
- Median Absolute Deviation: 0.07
- 1% percentile: -0.9245457077026367
- 99% percentile: 0.32145620346069204

No sources have both SDSS r (fiberMag) and RCS r (aperture) values.
No sources have both SDSS r (petroMag) and RCS r (total) values.
CFHT Megacam r (aperture) - SDSS r (fiberMag):
- Median: -0.21
- Median Absolute Deviation: 0.13
- 1% percentile: -0.85657958984375
- 99% percentile: 2.3280612945557015

CFHT Megacam r (total) - SDSS r (petroMag):
- Median: -0.21
- Median Absolute Deviation: 0.13
- 1% percentile: -0.85657958984375
- 99% percentile: 2.3280612945557015

GPC1 r (aperture) - SDSS r (fiberMag):
- Median: -0.42
- Median Absolute Deviation: 0.14
- 1% percentile: -1.1833213043212891
- 99% percentile: 0.7033475494384753

GPC1 r (total) - SDSS r (petroMag):
- Median: -0.42
- Median Absolute Deviation: 0.14
- 1% percentile: -1.1833213043212891
- 99% percentile: 0.7033475494384753

WFC i (aperture) - SDSS i (fiberMag):
- Median: -0.41
- Median Absolute Deviation: 0.08
- 1% percentile: -0.9962200164794922
- 99% percentile: 0.3547430038452155

WFC i (total) - SDSS i (petroMag):
- Median: -0.41
- Median Absolute Deviation: 0.08
- 1% percentile: -0.9962200164794922
- 99% percentile: 0.3547430038452155

No sources have both SDSS i (fiberMag) and RCS i (aperture) values.
No sources have both SDSS i (petroMag) and RCS i (total) values.
GPC1 i (aperture) - SDSS i (fiberMag):
- Median: -0.41
- Median Absolute Deviation: 0.12
- 1% percentile: -1.0375835609436035
- 99% percentile: 0.48758485794067447

GPC1 i (total) - SDSS i (petroMag):
- Median: -0.41
- Median Absolute Deviation: 0.12
- 1% percentile: -1.0375835609436035
- 99% percentile: 0.48758485794067447

WFC z (aperture) - SDSS z (fiberMag):
- Median: -0.35
- Median Absolute Deviation: 0.12
- 1% percentile: -1.2592236328125
- 99% percentile: 0.6335509490966799

WFC z (total) - SDSS z (petroMag):
- Median: -0.35
- Median Absolute Deviation: 0.12
- 1% percentile: -1.2592236328125
- 99% percentile: 0.6335509490966799

No sources have both SDSS z (fiberMag) and RCS z (aperture) values.
No sources have both SDSS z (petroMag) and RCS z (total) values.
CFHT Megacam z (aperture) - SDSS z (fiberMag):
- Median: -0.15
- Median Absolute Deviation: 0.20
- 1% percentile: -1.1121234893798828
- 99% percentile: 2.766660690307617

CFHT Megacam z (total) - SDSS z (petroMag):
- Median: -0.15
- Median Absolute Deviation: 0.20
- 1% percentile: -1.1121234893798828
- 99% percentile: 2.766660690307617

GPC1 z (aperture) - SDSS z (fiberMag):
- Median: -0.31
- Median Absolute Deviation: 0.17
- 1% percentile: -1.2946042251586913
- 99% percentile: 1.2326162338256823

GPC1 z (total) - SDSS z (petroMag):
- Median: -0.31
- Median Absolute Deviation: 0.17
- 1% percentile: -1.2946042251586913
- 99% percentile: 1.2326162338256823

III.b - Comparing J and K bands to 2MASS

The catalogue is cross-matched to 2MASS-PSC withing 0.2 arcsecond. We compare the UKIDSS total J and K magnitudes to those from 2MASS.

The 2MASS magnitudes are “Vega-like” and we have to convert them to AB magnitudes using the zero points provided on this page:

Band	Fν - 0 mag (Jy)
J	1594
H	1024
Ks	666.7

In addition, UKIDSS uses a K band whereas 2MASS uses a Ks (“short”) band, this page give a correction to convert the K band in a Ks band with the formula:

$$K_{s(2MASS)} = K_{UKIRT} + 0.003 + 0.004 * (J−K)_{UKIRT}$$

# The AB zero point is 3631 Jy
j_2mass_to_ab = 2.5 * np.log10(3631/1595)
k_2mass_to_ab = 2.5 * np.log10(3631/666.7)

twomass = Table.read("../../dmu0/dmu0_2MASS-point-sources/data/2MASS-PSC_Lockman-SWIRE.fits")
twomass_coords = SkyCoord(twomass['raj2000'], twomass['dej2000'])

idx, d2d, _ = twomass_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

twomass = twomass[mask]
ml_twomass_idx = idx[mask]

nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_ukidss_j'][ml_twomass_idx],
                labels=("2MASS J", "UKIDSS J (total)"))

UKIDSS J (total) - 2MASS J:
- Median: 0.02
- Median Absolute Deviation: 0.06
- 1% percentile: -0.8742653710843861
- 99% percentile: 0.779014798898522

ukidss_ks_like = master_catalogue['m_ukidss_k'] + 0.003 + 0.004 * (
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'])
nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, ukidss_ks_like[ml_twomass_idx],
                labels=("2MASS Ks", "UKIDSS Ks-like (total)"))

UKIDSS Ks-like (total) - 2MASS Ks:
- Median: 0.07
- Median Absolute Deviation: 0.08
- 1% percentile: -0.8243777712114841
- 99% percentile: 0.8419315067426179

Keeping only sources with good signal to noise ratio

From here, we are only comparing sources with a signal to noise ratio above 3, i.e. roughly we a magnitude error below 0.3.

To make it easier, we are setting to NaN in the catalogue the magnitudes associated with an error above 0.3 so we can't use these magnitudes after the next cell.

for error_column in [_ for _ in master_catalogue.colnames if _.startswith('merr_')]:
    column = error_column.replace("merr", "m")
    keep_mask = np.isfinite(master_catalogue[error_column])
    keep_mask[keep_mask] &= master_catalogue[keep_mask][error_column] <= 0.3
    master_catalogue[column][~keep_mask] = np.nan

IV - Comparing aperture magnitudes to total ones.

nb_ccplots(
    master_catalogue['m_cfht_megacam_r'],
    master_catalogue['m_ap_cfht_megacam_r'] - master_catalogue['m_cfht_megacam_r'],
    "r total magnitude (CFHT)", "r aperture mag - total mag (CFHT)",
    master_catalogue["stellarity"],
    invert_x=True
)

Number of source used: 2456857 / 4641777 (52.93%)

V - Color-color and magnitude-color plots

nb_ccplots(
    master_catalogue['m_wfc_g'] - master_catalogue['m_wfc_i'],
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'],
    "g - i (WFC)", "J - K (UKIDSS)",
    master_catalogue["stellarity"]
)

Number of source used: 78472 / 4641777 (1.69%)

nb_ccplots(
    master_catalogue['m_wfc_i'] - master_catalogue['m_irac1'],
    master_catalogue['m_wfc_g'] - master_catalogue['m_wfc_i'],
    "WFC i - IRAC1", "g - i (WFC)",
    master_catalogue["stellarity"]
)

Number of source used: 129249 / 4641777 (2.78%)

nb_ccplots(
    master_catalogue['m_cfht_megacam_u'] - master_catalogue['m_cfht_megacam_g'],
    master_catalogue['m_cfht_megacam_g'] - master_catalogue['m_cfht_megacam_r'],
    "u - g (CFHT)", "g - r (CFHT)",
    master_catalogue["stellarity"]
)

Number of source used: 2070789 / 4641777 (44.61%)

nb_ccplots(
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'],
    master_catalogue['m_wfc_g'] - master_catalogue['m_ukidss_j'],
    "J - K (UKIDSS)", "g - J (WFC, UKIDSS)",
    master_catalogue["stellarity"]
)

Number of source used: 101321 / 4641777 (2.18%)

nb_ccplots(
    master_catalogue['m_wfc_i'] - master_catalogue['m_wfc_z'],
    master_catalogue['m_wfc_z'] - master_catalogue['m_ukidss_j'],
    "i - z (WFC)", "z - J (WFC, UKIDSS)",
    master_catalogue["stellarity"]
)

Number of source used: 9237 / 4641777 (0.20%)

nb_ccplots(
    master_catalogue['m_irac3'] - master_catalogue['m_irac4'],
    master_catalogue['m_irac1'] - master_catalogue['m_irac2'],
    "IRAC3 - IRAC4", "IRAC1 - IRAC2",
    master_catalogue["stellarity"]
)

Number of source used: 38805 / 4641777 (0.84%)